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Record W3173598219 · doi:10.1002/onco.13890

Province-Wide Analysis of Patient-Reported Outcomes for Stage IV Non-Small Cell Lung Cancer

2021· article· en· W3173598219 on OpenAlexaffabout
Michael C. Tjong, Mark Doherty, Hendrick Tan, Wing C. Chan, Haoyu Zhao, Julie Hallet, Gail Darling, Biniam Kidane, Frances C. Wright, Alyson Mahar, Laura Davis, Victoria Delibasic, Ambika Parmar, Nicole Mittmann, Natalie G. Coburn, Alexander V. Louie

Bibliographic record

VenueThe Oncologist · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsCanadian Agency for Drugs and Technologies in HealthUniversity of ManitobaManitoba HealthResearch Institute in Oncology and HematologyHealth Sciences CentreCancerCare ManitobaToronto General HospitalInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineComorbidityLung cancerAnxietyNauseaPoisson regressionStage (stratigraphy)PopulationCancerInternal medicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In Ontario, Canada, patient-reported outcome (PRO) evaluation through the Edmonton Symptom Assessment System (ESAS) has been integrated into clinical workflow since 2007. As stage IV non-small cell lung cancer (NSCLC) is associated with substantial disease and treatment-related morbidity, this province-wide study investigated moderate to severe symptom burden in this population. MATERIALS AND METHODS: ESAS collected from patients with stage IV NSCLC diagnosed between 2007 and 2018 linked to the Ontario provincial health care system database were studied. ESAS acquired within 12 months following diagnosis were analyzed and the proportion reporting moderate to severe scores (ESAS ≥4) in each domain was calculated. Predictors of moderate to severe scores were identified using multivariable Poisson regression models with robust error variance. RESULTS: Of 22,799 patients, 13,289 (58.3%) completed ESAS (84,373 assessments) in the year following diagnosis. Patients with older age, with high comorbidity, and not receiving active cancer therapy had lower ESAS completion. The majority (94.4%) reported at least one moderate to severe symptom. The most prevalent were tiredness (84.1%), low well-being (80.7%), low appetite (71.7%), and shortness of breath (67.8%). Most symptoms peaked at diagnosis and, while declining, remained high in the following year. On multivariable analyses, comorbidity, low income, nonimmigrants, and urban residency were associated with moderate to severe symptoms. Moderate to severe scores in all ESAS domains aside from anxiety were associated with radiotherapy within 2 weeks prior, whereas drowsiness, low appetite and well-being, nausea, and tiredness were associated with systemic therapy within 2 weeks prior. CONCLUSION: This province-wide PRO analysis showed moderate to severe symptoms were prevalent and persistent among patients with metastatic NSCLC, underscoring the need to address supportive measures in this population especially around treatments. IMPLICATIONS FOR PRACTICE: In this largest study of lung cancer patient-reported outcomes (PROs), stage IV non-small cell lung cancer patients had worse moderate-to-severe symptoms than other metastatic malignancies such as breast or gastrointestinal cancers when assessed with similar methodology. Prevalence of moderate-to-severe symptoms peaked early and remained high during the first year of follow-up. Symptom burden was associated with recent radiation and systemic treatments. Early and sustained PRO collection is important to detect actionable symptom progression, especially around treatments. Vulnerable patients (e.g., older, high comorbidity) who face barriers in attending in-person clinic visits had lower PRO completion. Virtual PRO collection may improve completion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.334
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2021
Admission routes2
Has abstractyes

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